





'''simple docstring'''







from __future__ import annotations

import typing
from collections.abc import Iterable

import numpy as np

lowerCAmelCase_   :       Optional[int]       =     typing.Union[Iterable[float], Iterable[int], np.ndarray]  # noqa: UP007
lowerCAmelCase_   :       Union[str, Any]       =     typing.Union[np.floataa, int, float]  # noqa: UP007






def 					_lowerCamelCase							(					lowercase		: Vector       ,				lowercase		: Vector		)      ->      VectorOut:
				return np.sqrt(np.sum((np.asarray(_A		) - np.asarray(_A		)) ** 2		)		)






def 					_lowerCamelCase							(					lowercase		: Vector       ,				lowercase		: Vector		)      ->      VectorOut:
				return sum((va - va) ** 2 for va, va in zip(_A       ,				_A		)		) ** (1 / 2)


if __name__ == "__main__":






					def 					_lowerCamelCase							(					)      ->      None:
									from timeit import timeit

									print("Without Numpy"		)
									print(
									    timeit(
									        "euclidean_distance_no_np([1, 2, 3], [4, 5, 6])"       ,				number=1_0000       ,				globals=globals()       ,				)		)
									print("With Numpy"		)
									print(
									    timeit(
									        "euclidean_distance([1, 2, 3], [4, 5, 6])"       ,				number=1_0000       ,				globals=globals()       ,				)		)



					benchmark()




from ...configuration_utils import PretrainedConfig
from ...utils import logging


__UpperCamelCase     		=							logging.get_logger(__name__)

__UpperCamelCase     		=							{
    'microsoft/trocr-base-handwritten': (
        'https://huggingface.co/microsoft/trocr-base-handwritten/resolve/main/config.json'
    ),
    # See all TrOCR models at https://huggingface.co/models?filter=trocr
}







class 							lowerCamelCase__						(				UpperCAmelCase   ):






			"""simple docstring"""






			_UpperCamelCase		:       Dict 			=		'trocr'
			_UpperCamelCase		:       Union[str, Any] 			=		['past_key_values']
			_UpperCamelCase		:       Union[str, Any] 			=		{
			    'num_attention_heads': 'decoder_attention_heads',
			    'hidden_size': 'd_model',
			    'num_hidden_layers': 'decoder_layers',
			}




			def __init__(				self   ,    snake_case=50265   ,    snake_case=1024   ,    snake_case=12   ,    snake_case=16   ,    snake_case=4096   ,    snake_case="gelu"   ,    snake_case=512   ,    snake_case=0.1   ,    snake_case=0.0   ,    snake_case=0.0   ,    snake_case=2   ,    snake_case=0.02   ,    snake_case=0.0   ,    snake_case=True   ,    snake_case=False   ,    snake_case=True   ,    snake_case=True   ,    snake_case=1   ,    snake_case=0   ,    snake_case=2   ,    **snake_case   ,    ):

						'''simple docstring'''


						UpperCamelCase__       =							vocab_size
						UpperCamelCase__       =							d_model
						UpperCamelCase__       =							decoder_layers
						UpperCamelCase__       =							decoder_attention_heads
						UpperCamelCase__       =							decoder_ffn_dim
						UpperCamelCase__       =							activation_function
						UpperCamelCase__       =							max_position_embeddings
						UpperCamelCase__       =							dropout
						UpperCamelCase__       =							attention_dropout
						UpperCamelCase__       =							activation_dropout
						UpperCamelCase__       =							init_std
						UpperCamelCase__       =							decoder_layerdrop
						UpperCamelCase__       =							use_cache
						UpperCamelCase__       =							scale_embedding
						UpperCamelCase__       =							use_learned_position_embeddings
						UpperCamelCase__       =							layernorm_embedding

						super().__init__(
						    pad_token_id=snake_case   ,    bos_token_id=snake_case   ,    eos_token_id=snake_case   ,    decoder_start_token_id=snake_case   ,    **snake_case   ,    )


